Huihui Chen

FlierMeet: A Mobile Crowdsensing System for Cross-Space Public Information Reposting, Tagging, and Sharing

Community bulletin boards serve an important function for public information sharing in modern society. Posted fliers advertise services, events, and other announcements. However, fliers posted offline suffer from problems such as limited spatial-temporal coverage and inefficient search support. In recent years, with the development of sensor-enhanced mobile devices, mobile crowd sensing (MCS) has been used in a variety of application areas. This paper presents FlierMeet, a crowd-powered sensing system for cross-space public information reposting, tagging, and sharing. The tags learned are useful for flier sharing and preferred information retrieval and suggestion. Specifically, we utilize various contexts (e.g., spatio-temporal info, flier publishing/reposting behaviors, etc.) and textual features to group similar reposts and classify them into categories. We further identify a novel set of crowd-object interaction hints to predict the semantic tags of reposts. To evaluate our system, 38 participants were recruited and 2,035 reposts were captured during an eight-week period. Experiments on this dataset showed that our approach to flier grouping is effective and the proposed features are useful for flier category/semantic tagging.

Worker-Contributed Data Utility Measurement for Visual Crowdsensing Systems

Visual crowdsensing is successfully applied in numerous application areas, yet little work has been done on measuring and improving the quality of worker contributed visual data. Rather than evaluating the visual quality based on traditional metrics such as resolution, we focus on data diversity, which is crucial for a broad stream of visual crowdsensing tasks. Two representative diversity-oriented task types are studied, namely static object imagery and evolving event photography. The former aims to collect multi-facet/aspect yet low redundant data about a stationary object, while the latter wants to detect and collect details of key scenes throughout an event. We link these quality needs with data utility and propose a unified visual crowdsensing framework called UtiPay. Data utility is characterized by the macro and micro diversity needs: at the macro level, the pyramid-tree approach is proposed for multi-attribute-based data grouping; at the micro level, we use several strategies for intra-group data selection and worker contribution measurement. To study the impact of our proposed utility measurement approaches, we propose two utility-enhanced payment schemes as incentive mechanisms: Uti and Uti-Bid. Experiments over several user studies with a total of 43 subjects validate the performance of UtiPay for measuring and enhancing the data quality of visual crowdsensing tasks.

TaskMe: Toward a dynamic and quality-enhanced incentive mechanism for mobile crowd sensing

Abstract Incentive is crucial to the success of mobile crowd sensing (MCS) systems. Over the different manners of incentives, providing monetary rewards has been proved quite useful. However, existing monetary-based incentive studies (e.g., the reverse auction based methods) mainly encourage user participation, whereas sensing quality is often neglected. First, the budget setting is static and may not meet the sensing contexts or user anticipation. Second, they do not measure the quality of data contributed. Third, the design of most incentive schemes is quantity- or cost-focused and not quality-oriented. To address these issues, we propose a novel MCS incentive mechanism called TaskMe. An LBSN (location-based social network)-powered model is leveraged for dynamic budgeting and proper worker selection, and a combination of multi-facet quality measurements and a multi-payment-enhanced reverse auction scheme are used to improve sensing quality. Experiments on several user studies and the crawled dataset validate TaskMes effectiveness.

A Generic Framework for Constraint-Driven Data Selection in Mobile Crowd Photographing

Mobile crowd photographing (MCP) is an emerging area of interest for researchers as the built-in cameras of mobile devices are becoming one of the commonly used visual logging approaches in our daily lives. In order to meet diverse MCP application requirements and constraints of sensing targets, a multifacet task model should be defined for a generic MCP data collection framework. Furthermore, MCP collects pictures in a distributed way in which a large number of contributors upload pictures whenever and wherever it is suitable. This inevitably leads to evolving picture streams. This paper investigates the multiconstraint-driven data selection problem in MCP picture aggregation and proposes a pyramid-tree (PTree) model which can efficiently select an optimal subset from the evolving picture streams based on varied coverage needs of MCP tasks. By utilizing the PTree model in a generic MCP data collection framework, which is called CrowdPic, we test and evaluate the effectiveness, efficiency, and flexibility of the proposed framework through crowdsourcing-based and simulation-based experiments. Both the theoretical analysis and simulation results indicate that the PTree-based framework can effectively select a subset with high utility coverage and low redundancy ratio from the streaming data. The overall framework is also proved flexible and applicable to a wide range of MCP task scenarios.

The Emergence of Visual Crowdsensing: Challenges and Opportunities

Visual crowdsensing (VCS), which leverages built-in cameras of smart devices to attain informative and comprehensive sensing of interesting targets, has become a predominant sensing paradigm of mobile crowdsensing (MCS). Compared to MCS tasks using other sensing modalities, VCS faces numerous unique issues, such as multi-dimensional coverage needs, data redundancy identification and elimination, low-cost transmission, as well as high data processing cost. This paper characterizes the concepts, unique features, and novel application areas of VCS, and investigates its challenges and key techniques. A generic framework for VCS systems is then presented, followed by discussions about the future directions of crowdsourced picture transmission and the experimental setup in VCS system evaluation.

Toward real-time and cooperative mobile visual sensing and sharing

Mobile social media enables people to record ongoing physical events they witness and share them instantaneously online. However, since these event pictures are often individually provided, they are typically fragmented and possess high redundancy. Though there have been studies about visual event summarization, they pay little attention to collaborative sensing, subevent detection, and event summary. In this paper, we present several building blocks for a cooperative visual sensing and sharing system. We create a virtual opportunistic community associated with an event, where members collaborate to cover different aspects of the event. More specifically, a crowd-powered approach is first used to localize the event. We then propose three subevent segmentation methods based on crowd-event interaction patterns. Based on the segmentation results, we summarize the event at two levels: multi-facet subevent summary and crowd-behavior-based highlights. Experiments over 21 online datasets and two real world datasets demonstrate the effectiveness of our approaches.

FlierMeet: cross-space public information reposting with mobile crowd sensing

Bulletin boards serve an important function for public information sharing. Posted fliers advertise services, events and other announcements. However, fliers posted offline suffer from problems such as limited spatial-temporal coverage and inefficient search aid. In recent years, with the development of sensor-enhanced mobile devices, mobile crowd sensing has been used in a variety of application areas. In this paper we present FlierMeet, a crowd-powered sensing system for crossspace public information reposting, tagging and sharing. The tags are auto-labeled based on a set of visual and crowd-object interaction features. Initial deployments and experiments prove the effectiveness of our system.

CrowdPic: A Multi-coverage Picture Collection Framework for Mobile Crowd Photographing

This paper proposes a generic task-driven data collection framework, named as Crowd Pic, for Mobile Crowd Photographing (MCP) - a widely used technique in crowd sensing. In order to meet diverse MCP application requirements (e.g. Spatio-temporal contexts, single or multiple shooting angles to a sensing target), a multifaceted task model with collection constraints is provided in Crowd Pic. Meanwhile, a pre-selection process is necessary to prevent mobile clients from uploading redundant pictures so as to reduce the overhead traffic and maintain the sensing quality. To address this issue, we developed a pyramid-tree (PTree) model which can select maximum diversified subset from the evolving picture streams based on multiple coverage requirements and constraints defined in MCP tasks by data requesters. Crowd sourcing-based and simulation-based methods are both used to evaluate the effectiveness, efficiency and flexibility of the proposed framework. The experimental results indicate that the PTree method can efficiently assess redundant pictures and effectively select minimal subset with high coverage from the streaming picture according to various coverage needs, and the whole framework is applicable to a wide range of use scenarios.

PicPick: a generic data selection framework for mobile crowd photography

Mobile crowd photography (MCP) is a widely used technique in crowd sensing. In MCP, a picture stream is generated when delivering intermittently to the backend server by participants. Pictures contributed later in the stream may be semantically or visually relevant to previous ones, which can result in data redundancy. To meet diverse constraints (e.g., spatiotemporal contexts, single or multiple shooting angles) on the data to be collected in MCP tasks, a data selection process is needed to eliminate data redundancy and reduce network overhead. This issue has little been investigated in existing studies. To address this requirement, we propose a generic data collection framework called PicPick. It first presents a multifaceted task model that allows for varied MCP task specification. A pyramid tree (PTree) method is further proposed to select an optimal set of pictures from picture streams based on multi-dimensional constraints. Experimental results on two real-world datasets indicate that PTree can effectively reduce data redundancy while maintaining the coverage requests, and the overall framework is flexible.

Cyber-physical-social collaborative sensing: from single space to cross-space

The development of wireless sensor networking, social networking, and wearable sensing techniques has advanced the boundaries of research on understanding social dynamics. Collaborative sensing, which utilizes diversity sensing and computing abilities across different entities, has become a popular sensing and computing paradigm. In this paper, we first review the history of research in collaborative sensing, which mainly refers to single space collaborative sensing that consists of physical, cyber, and social collaborative sensing. Afterward, we extend this concept into cross-space collaborative sensing and propose a general reference framework to demonstrate the distinct mechanism of cross-space collaborative sensing. We also review early works in cross-space collaborative sensing, and study the detail mechanism based on one typical research work. Finally, although cross-space collaborative sensing is a promising research area, it is still in its infancy. Thus, we identify some key research challenges with potential technical details at the end of this paper.